A new study by the International Livestock Research Institute (ILRI) has demonstrated that drones equipped with methane sensors can reliably measure greenhouse-gas emissions from grazing livestock across Africa.
The findings — published in the journal Biogeosciences — reveal that drone-based measurements produce estimates of methane emissions comparable to those generated by classic inventory methods used by the Intergovernmental Panel on Climate Change (IPCC), opening a path toward more accurate and context-specific emissions monitoring in pastoral systems.
The researchers carried out field tests in early 2024 at ILRI’s Kapiti Research Station — near Machakos, Kenya — flying a drone over various herds of cattle, sheep, goats, and for the first time in Africa, camels.
Equipped with a mid-infrared CH₄ gas sensor, the drone captured atmospheric methane plumes emitted by ruminants as they grazed and rested.
Using a novel “Bayesian inference” approach, the team combined drone concentration readings with wind and dispersion data (from a ground-based flux tower) to model methane emission rates. These Bayesian estimates matched the IPCC Tier 2 values for enteric methane emissions — a standard reference for national greenhouse-gas inventories.
Beyond alignment with conventional estimates, the drone-based method also demonstrated two important strengths. First, it picked up fluctuations in emissions over the course of the day — notably increases associated with grazing — indicating that enteric methane can vary with animal activity, and that drones can track these dynamics. Second, the method proved robust not only for large herds (cattle) but also for smaller or weaker emitters such as sheep or goats, where conventional mass-balance methods often fail.
In practical terms, the team successfully measured emissions from herds of roughly 100 cattle, 170 sheep, 140 goats, and 40 camels during their test flights, demonstrating the method’s applicability under real-world farming and pastoral conditions.
The scientists also explored using hyperspectral satellite data to identify “hotspot” areas where livestock activity (and thus methane emissions) might be happening, to guide future drone flights.
These developments suggest a powerful new tool for countries across Africa — and beyond — to generate better, more accurate livestock methane data tailored to local conditions. With further refinement, drone-based methane monitoring could become a key component of national greenhouse-gas inventories and efforts to design climate-smart livestock systems.
